搜索资源列表
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在目标检测中有关高斯背景建模方法的实现和国际上先进方法的介绍-Adaptive background mixture models for real-time tracking（高斯背景建模方法）
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A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel.
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A paper on A Pixel wise Object Tracking Algorithm with Target and Background Sample
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结合了背影恢复技术和目标跟踪技术进行视频分割算法，分背景重建、运动目标提取、跟踪技术的使用和后处理4个步骤-Combined with the back of recovery techniques and target tracking technology for video segmentation algorithm, sub-background reconstruction, moving object extraction, tracking the use and post-pr
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dissertation about moving object detection and tracking
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在移动目标检测中的，在高斯背景建模的基础上滤除阴影OpenCV-An improved adaptive background mixture model for real-time tracking with shadow detection（在高斯背景建模的基础上滤除阴影OpenCV
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介绍了一种常用的Kim目标分割方法,并
针对其不足,对Kim方法进行了改进,将连续两帧的差分图像和背景差分图像直接相乘得到灰度图
像,然后对该灰度图像进行阈值分割来获取目标区域模板,再基于灰度加权图像模板匹配法实现目标
跟踪
-A common Kim target segmentation, and for its shortcomings, Kim method improvements, the two consecutive frames of the different
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Abstract—Several algorithms have been developed for tracking
formant frequency trajectories of speech signals, however most of
these algorithms are either not robust in real-life noise environments
or are not suitable for real-time implementati
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